1. Technical Field
The invention relates to a pattern recognition device or classifier. Image processing systems often contain pattern recognition devices (classifiers).
2. Description of the Prior Art
Pattern recognition systems, loosely defined, are systems capable of distinguishing between various classes of real world stimuli according to their divergent characteristics. A number of applications require pattern recognition systems, which allow a system to deal with unrefined data without significant human intervention. By way of example, a pattern recognition system may attempt to classify individual letters to reduce a handwritten document to electronic text. Alternatively, the system may classify spoken utterances to allow verbal commands to be received at a computer console.
A typical prior art classifier is trained over a plurality of output classes by a set of training data. The training data is processed, data relating to features of interest are extracted, and training parameters are derived from this feature data. As the system receives an input associated with one of a plurality of classes, it analyzes its relationship to each class via a classification technique based upon these training parameters. From this analysis, the system produces an output class and an associated confidence value.
In some applications, such as optical character recognition, the output classes stay substantially the same. In many others, however, output classes can change relatively frequently. In such systems, it is often necessary to change the output classes to reflect changes in the data population. In a prior art system as described above, this requires retraining the entire classification system. Such retraining is time consuming and potentially expensive. A system capable of adding and removing output classes without system-wide retraining would be desirable.